9th November 2017
Overview
Objectives
- Characterise the HaCaT immunopeptidome \(\pm\)DNCB.
- Characterise HaCaT protein turnover/abundance \(\pm\)DNCB.
- Infer relationship(s) between DNCB, peptidome, protein turn over and protein abundance.
- Identify (and test) candidate immunogenic peptides.
Method development
Capturing MHC I peptides | Observing protein turnover
Peptidome
Peptidome results 1
Venn diagram
Peptidome results 2
Modified peptide spectra
Protein turnover
Protein turnover
Protein turnover
Protein turnover
Protein turnover
Protein turnover
Protein turnover
Experimental next steps
Complete HaCaT dataset | Start HLA-A2 transfectant dataset
Method development
Quantifying our uncertainty
Peptidome areas of uncertainty
- How many peptides are there per treatment?
- Is the size of the peptidome repertoire different between treatments?
- Can we estimate the probability that a peptide is unique to one treatment?
Paul A. Smith, S3RI University of Southampton
Protein turnover
Binary general linear model
dat_turnover[c(1:5,902:907),1:3]
## # A tibble: 11 x 3 ## Peptide Treatment Turnover.Rate ## <int> <chr> <dbl> ## 1 1 Control 0.149 ## 2 0 Control 0.513 ## 3 0 Control 0.491 ## 4 1 Control 0.614 ## 5 1 Control 0.445 ## 6 0 DNCB 0.111 ## 7 1 DNCB 0.794 ## 8 1 DNCB 0.704 ## 9 0 DNCB 0.268 ## 10 0 DNCB 0.393 ## 11 1 DNCB 0.802
Protein turnover
Binary general linear model
\(Y_i = \beta_0 + \beta_1.X_i + \epsilon_i\)
\(X_i\) : protein turnover rate for protein \(X\).
\(Y_i\) : probability of observing peptide from protein \(X\) at the cell surface.
\(\beta_0\) : expected value of \(Y_i\) under given treatment.
\(\beta_1\) : increase in probability of \(Y_i\) due to increase in turnover rate.
\(\epsilon_i\) : is the residual error term.
Models for \(\beta_0\) and \(\beta_1\)